BFCL

The BFCL dataset/benchmark is used to evaluate the effectiveness of tool-calling capabilities in large language models, focusing on their ability to retrieve and utilize demonstrations for specific tasks.

Papers using BFCL (14)

Skill or Skip? Learning Selective Skill Invocation in Agentic Tasks via Dual-Granularity Preference Learning2026Scaling Agentic Capabilities via Grounded Interaction Synthesis2026PACT: Privileged Trace Co-Training for Multi-Turn Tool-Use Agents2026Looking Is Not Picking: An Attention-Segment Account of Tool-Selection Failures in LLM Agents2026TwinRouterBench: Fast Static and Live Dynamic Evaluation for Realistic Agentic LLM Routing2026Notation Matters: A Benchmark Study of Token-Optimized Formats in Agentic AI Systems2026How Many Tools Should an LLM Agent See? A Chance-Corrected Answer2026Boosting Tool-Calling Capabilities of Large Language Models via a Novel In-Context Learning Approach2026TSCG: Deterministic Tool-Schema Compilation for Agentic LLM Deployments2026CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution2026Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models2026Try, Check and Retry: A Divide-and-Conquer Framework for Boosting Long-context Tool-Calling Performance of LLMs2026Beyond Max Tokens: Stealthy Resource Amplification via Tool Calling Chains in LLM Agents2026Linguistic and Argument Diversity in Synthetic Data for Function-Calling Agents2026